Sensor control for multi-target tracking using Cauchy-Schwarz divergence

Beard, M.; Vo, Ba-Ngu; Vo, Ba Tuong; Arulampalam, S.

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Authors

Beard, M.

Vo, Ba-Ngu

Vo, Ba Tuong

Arulampalam, S.

Date

2015

Type

Conference Paper

Metadata

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Citation

Beard, M. and Vo, B. and Vo, B.T. and Arulampalam, S. 2015. Sensor control for multi-target tracking using Cauchy-Schwarz divergence, in Proceedings of the 18th International Conference on Information Fusion (Fusion), Jul 6-9 2015, pp. 937-944. Washington, DC: IEEE.

Source Title

2015 18th International Conference on Information Fusion, Fusion 2015

ISBN

9780982443866

School

Department of Electrical and Computer Engineering

URI

http://hdl.handle.net/20.500.11937/26907

Collection

Curtin Research Publications

Abstract

In this paper, we propose a method for optimal stochastic sensor control, where the goal is to minimise the estimation error in multi-object tracking scenarios. Our approach is based on an information theoretic divergence measure between labelled random finite set densities. The multi-target posteriors are generalised labelled multi-Bernoulli (GLMB) densities, which do not permit closed form solutions for traditional information divergence measures such as Kullback-Leibler or Rényi. However, we demonstrate that the Cauchy-Schwarz divergence admits a closed form solution for GLMB densities, thus it can be used as a tractable objective function for multi-target sensor control. This is demonstrated with an application to sensor trajectory optimisation for bearings-only multi-target tracking.